arxiv.org/abs/2507.13334v2
00:00:00 Introduction
00:02:46 Related Work
00:05:43 Why Context Engineering?
00:09:36 Foundational Components
00:17:52 System Implementations
00:29:05 Evaluation
00:35:30 Future Directions and Open Challenges
00:41:13 Conclusion
Source: RESTful Web APIs by Leonard Richardson and Mike Amundsen with a Foreword by Sam Ruby
00:00:00 Introduction
00:01:57 Surfing the Web
00:05:11 A Simple API
00:08:46 Resources and Representations
00:13:07 Hypermedia
00:17:40 Domain-Specific Designs
00:22:28 The Collection Pattern
00:27:04 Pure-Hypermedia Designs
00:31:48 Profiles
00:36:34 The Design Procedure
00:41:39 The Hypermedia Zoo
00:46:18 HTTP for APIs
00:50:51 Resource Description and Linked Data
00:55:42 CoAP: REST for Embedded Systems
01:00:19 Appendix C: An API Designer's Guide to the Fielding Dissertation
01:04:43 Glossary
00:00:00 Introducing Knowledge Graphs
00:03:15 Organizing Principles for Building Knowledge Graphs
00:07:37 Graph Databases
00:11:58 Loading Knowledge Graph Data
00:16:08 Integrating Knowledge Graphs with Information Systems
00:20:43 Enriching Knowledge Graphs with Data Science
00:24:49 Graph-Native Machine Learning
00:28:54 Mapping Data with Metadata Knowledge Graphs
00:32:41 Identity Knowledge Graphs
00:36:35 Pattern Detection Knowledge Graphs
00:40:53 Dependency Knowledge Graphs
00:45:31 Semantic Search and Similarity
00:49:51 Talking to Your Knowledge Graph
00:54:16 From Knowledge Graphs to Knowledge Lakes
00:00:00 Introduction
00:05:40 Chapter 1 - Hierarchy is Everything
00:13:41 Chapter 2 - Layout and Spacing
00:20:30 Chapter 3 - Designing Text
00:28:49 Chapter 4 - Working with Color
00:36:34 Chapter 5 - Creating Depth
00:42:04 Chapter 6 - Working with Images
00:47:23 Chapter 7 - Finishing Touches
00:54:09 Chapter 8 - Leveling Up
00:00:00 Introduction
00:01:02 Discrete-Time Signals and Systems
00:02:20 The z-Transform and Its Application to the Analysis of LTI Systems
00:03:32 Frequency Analysis of Signals and Systems
00:04:41 The Discrete Fourier Transform: Its Properties and Applications
00:05:49 Efficient Computation of the DFT: Fast Fourier Algorithms
00:06:50 Implementation of Discrete-Time Systems
00:00:00 Domain-Driven Design
00:01:14 Concept and Principles
00:03:56 Building Blocks of DDD
00:07:06 Implementation of DDD
00:10:13 Implementation of DDD
00:10:33 DDD and Other Software Development Approaches
00:12:05 Criticism and Limitations of DDD
00:00:00 What is AI Ethics?
00:02:48 An Ethical Platform for a Responsible AI Project
00:04:37 The SUM Values
00:06:23 The FAST Track Principles
00:13:59 Securing Responsible Delivery Through Human-Centered Implementation
00:16:00 Conclusion
00:00:00 Introduction
00:00:54 Definitions and Examples
00:01:58 Paths and Cycles
00:03:42 Trees
00:05:00 Planarity
00:06:23 Colouring Graphs
00:07:29 Matching, Marriage, and Menger's Theorem
00:08:51 Matroids
00:00:00 Elasticsearch
00:02:58 Life Inside a Cluster
00:05:51 Data In, Data Out
00:09:35 Distributed Document Store
00:11:56 Searching—The Basic Tools
00:14:05 Mapping and Analysis
00:16:51 Full-Body Search
00:19:01 Sorting and Relevance
00:21:19 Distributed Search Execution
00:23:10 Index Management
00:25:41 Inside a Shard
00:00:00 Introduction to Docker
00:02:06 Installing Docker
00:03:17 Getting Started with Docker
00:04:42 Working with Docker Images and Repositories
00:06:29 Testing with Docker
00:08:02 Building Services with Docker
00:09:36 Docker Orchestration and Service Discovery
00:11:22 Using the Docker API
00:12:21 Getting Help and Extending Docker
00:00:00 What Is Observability?
00:01:30 How Debugging Practices Differ Between Observability and Monitoring
00:02:47 Lessons from Scaling Without Observability
00:04:20 How Observability Relates to DevOps, SRE, and Cloud Native
00:05:49 Structured Events Are the Building Blocks of Observability
00:07:21 Stitching Events into Traces
00:08:48 Instrumentation with OpenTelemetry
00:10:31 Analyzing Events to Achieve Observability
00:12:24 How Observability and Monitoring Come Together
00:14:00 Applying Observability Practices in Your Team
00:15:45 Observability-Driven Development
00:17:07 Using Service-Level Objectives for Reliability
00:18:45 Acting on and Debugging SLO-Based Alerts
00:20:20 Observability and the Software Supply Chain
00:21:55 Build Versus Buy and Return on Investment
00:23:50 Efficient Data Storage
00:26:26 Cheap and Accurate Enough: Sampling
00:28:38 Telemetry Management with Pipelines
00:30:25 The Business Case for Observability
00:32:06 Observability's Stakeholders and Allies
00:33:51 An Observability Maturity Model
00:35:42 Where to Go from Here
00:00:00 What is GraphQL
00:02:09 Graph Theory
00:03:53 The GraphQL Query Language
00:06:01 Designing a Schema
00:08:08 Creating a GraphQL API
00:10:05 GraphQL Clients
00:12:11 GraphQL in the Real World
00:00:00 Introduction
00:00:30 It All Starts with a Good Question
00:02:41 Tidying Up Your Data
00:03:40 Giving Your Data a Quick Health Check
00:04:21 Exploring Your Data
00:05:19 Making Sense of It All with Models
00:06:26 Predicting the Future with Machine Learning
00:07:44 The Quest for Causality
00:08:40 Writing Up Your Analysis
00:09:15 Creating Great Figures
00:10:11 Presenting Your Data
00:10:56 Making Your Work Reproducible
00:00:00 Introduction
00:00:55 Commodity Hardware Today
00:05:39 CPU Caches
00:10:46 Virtual Memory
00:13:02 NUMA Support
00:14:31 What Programmers Can Do
00:19:26 Memory Performance Tools
00:21:05 Upcoming Technology
00:00:00 Introduction
00:00:57 How much compute is used for frontier reasoning training?
00:03:04 What does reasoning compute scale mean for AI progress?
00:03:58 Can reasoning actually scale?
Josh You (2025), "How far can reasoning models scale?". Published online at epoch.ai. Retrieved from: 'https://epoch.ai/gradient-updates/how-far-can-reasoning-models-scale' [online resource]
00:00:00 Chapter 1.1: What is MCP?
00:01:41 Chapter 1.2: Why Was MCP Created?
00:03:42 Chapter 1.3: MCP Architecture Overview
00:05:26 Chapter 1.4: Tools, Resources, and Prompts
00:07:33 Section 2: MCP Projects
00:07:50 Project 1: 100% Local and Private MCP Client
00:08:29 Project 2: MCP-powered Agentic RAG
00:09:16 Project 3: MCP-powered Financial Analyst
00:10:09 Project 4: MCP-powered Voice Agent
00:11:11 Project 5: A Unified MCP Server with MindsDB
00:11:55 Project 6: MCP-powered Shared Memory for Claude and Cursor
00:12:41 Project 7: MCP-powered RAG over Complex Documents
00:13:19 Project 8: MCP-powered Synthetic Data Generator
00:14:12 Project 9: MCP-powered Deep Researcher
00:15:07 Project 10: MCP-powered RAG over Videos
00:15:58 Project 11: MCP-powered Audio Analysis Toolkit
Source: MCP illustrated guidebook by Daily Dose of Data Science
Based on MATTHEW FOWLER: Python Concurrency with asyncio
00:00:00 Python concurrency with asyncio
00:02:17 asyncio basics
00:04:47 A first asyncio application
00:06:35 Concurrent web requests
00:08:16 Non-blocking database drivers
00:09:48 Handling CPU-bound work
00:11:48 Handling blocking work with threads
00:13:31 Streams
00:14:48 Web applications
00:16:32 Microservices
00:18:29 Synchronization
00:20:25 Asynchronous queues
00:22:19 Managing subprocesses
00:23:55 Advanced asyncio
Based on Neil Madden: API Security in Action
00:00:00 What is API security?
00:02:41 Secure API development
00:04:38 Securing the Natter API
00:06:08 Session cookie authentication
00:07:56 Modern token-based authentication
00:09:56 Self-contained tokens and JWTs
00:11:46 OAuth2 and OpenID Connect
00:13:43 Identity-based access control
00:15:07 Capability-based security and macaroons
00:16:43 Microservice APIs in Kubernetes
00:18:20 Securing service-to-service APIs
00:19:38 Securing IoT communications
00:21:15 Securing IoT APIs
Source: Innovation Endeavors
00:00:00 The Big Picture in 2025
00:01:26 How We Got Here
00:03:33 A Deep Dive into the Models
00:06:52 A Deep Dive into the Models
00:07:37 Use Cases & Applications - Where AI is Making an Impact
00:09:52 Building AI Products - From Models to Systems
00:12:59 Market Structure & Dynamics
00:15:14 What's Next? The AI-Native Organization
00:17:37 What We're Excited to See Built
00:00:00 Caching
00:00:37 Chaptertransition
00:00:37 What is Distributed Caching, Anyway?
00:01:40 Chaptertransition
00:01:41 How to Do Caching the Right Way: Key Strategies
00:04:02 Chaptertransition
00:04:03 Choosing the Right Tool for the Job
00:05:26 Chaptertransition
00:05:27 The Performance Payoff: Cache vs. Database
00:06:32 Chaptertransition
00:06:33 In Conclusion: Caching is a Must-Have
00:07:03 Outro
Source: Designing Resilient Systems: A Guide to Distributed Caching for Modern Applications
